Market makers offer trades through order book & make money on bid-ask spreads paid by takers.

0x is a non-custodial exchange protocol built on Ethereum. 0x was founded by Amir Bandeali & Will Warren and launched in 2016.

ZRX tokenholders own and govern the protocol. They currently do not take any of the total trading fees paid by traders (revenue). 0x’s investors include Coinbase Ventures, Jump Capital, Pantera Capital, etc.

governance

github

twitter

discord

etherscan

website

Exchanges (DEX)

# 0x Active users (monthly) Methodology

## 1. Overview

The 0x Monthly Active Users (MAU) metric measures the number of unique users who have executed trades on the 0x decentralized exchange protocol within any 30-day rolling window. This metric provides insight into the protocol's user engagement and adoption across multiple blockchain networks. The calculation uses a rolling 30-day window approach with HyperLogLog algorithms for efficient distinct user counting, and provides dimensional breakdowns by chain, user type, and product categories.

## 2. Data sources

The calculation relies on the following data sources:
- **0x protocol transaction data**: Raw trading transaction records from 0x protocol's scraper system, containing transaction details, timestamps, chain identifiers, and user addresses
- **Cross-chain trading events**: Transaction data spans multiple blockchain networks where 0x protocol operates
- **Blockchain metadata**: Chain identifiers and availability information to ensure data quality and proper chain filtering

## 3. Step-by-step calculation

### Component A: User event extraction
- **Purpose**: Identifies and normalizes all trading events performed by users on 0x protocol
- **Logic**: Extracts distinct trading events from raw 0x transaction data, identifying the `taker` address as the user performing the trade. Each event includes the transaction timestamp, transaction hash, log index for uniqueness, blockchain chain identifier, and the user address. All users are classified as 'Trader' type within the 'exchanges' market sector and 'dex' business segment, with no product-level segmentation applied
- **Key fields**: `block_timestamp` for event timing, `transaction_hash` and `log_index` for event uniqueness, `chain` for blockchain identification, `taker` as the `user_address` performing the trade

### Component B: Daily active user calculation  
- **Purpose**: Aggregates user events into daily distinct user counts per chain and user type
- **Logic**: Groups trading events by day using timestamp truncation to daily granularity, then identifies distinct user addresses per day, chain, and user type combination. Uses HyperLogLog initialization with precision 15 to create efficient sketches for distinct counting. Applies chain availability filtering to ensure only valid chain data is included and enforces a data cutoff date
- **Key fields**: `timestamp` truncated to daily granularity, `timestamp_grain` set to 'day', `chain` identifier, `user_type` classification, `product_id` (null for 0x), `user_dau` containing the user address for distinct counting

### Component C: Rolling window creation
- **Purpose**: Creates 30-day sliding windows to calculate monthly active users for each day
- **Logic**: Generates a continuous datespine covering all days from the earliest data through yesterday, then creates joins between each day and its previous 29 days to form 30-day windows. Uses array generation functions to create the day-to-day mappings, ensuring no days are skipped even if they have zero activity. Applies incremental processing logic with safety margins for data freshness
- **Key fields**: `timestamp` as the window end date, `joinday` representing each day within the 30-day lookback period

### Component D: MAU aggregation with dimensional breakdowns
- **Purpose**: Calculates distinct users across 30-day windows and creates multiple dimensional views
- **Logic**: Merges HyperLogLog sketches across each 30-day window to count distinct users, then extracts the final distinct count. Creates four separate dimensional breakdowns: project-level (all dimensions null), chain-level (grouping by `chain_id`), user type-level (grouping by `user_type`), and product-level (grouping by `product_id`). Each breakdown nullifies non-applicable dimensions using safe casting
- **Key fields**: `user_mau` as the final distinct user count, `dimensions` indicating the breakdown type ('project_dbt_tag', 'chain', 'user_type', 'product_id'), dimension-specific fields (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a single result set with multiple dimensional views of the same underlying MAU calculation
- **Time aggregation**: Uses daily timestamp boundaries with 30-day rolling windows, ensuring consistent daily MAU values that represent unique users in the trailing 30-day period
- **Data quality controls**: Applies incremental processing with configurable lookback periods, uses the `finalize_metric_user_mau` macro for schema standardization, and implements fill-zero strategy for missing days
- **Final calculation**: The MAU value represents the distinct count of user addresses that performed at least one trade on 0x protocol within any 30-day rolling period, calculated using HyperLogLog merge and extract operations for computational efficiency

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 30-day window.

0xZRX

Archived

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown